Recruiting, Talent

What Are Talent Analytics, and How Can You Use Them?

Do you use data and have metrics related to sourcing talent? For example, do you know which of your recruiting pipelines generates the highest percentage of hires? Do you continue to use data to assess employee productivity, engagement, and retention? What about using and analyzing data to ensure the right staffing levels or to know in advance when to hire new employees? Or to determine which employee may be best suited for a given role? Or which employee is most likely to quit next?

All of these types of data analysis make up the field of talent analytics. Data has become the driving force behind many organizational decisions, and HR managers are poised to play a big part of that—as many of course are already doing! As talent analytics grows, we’re seeing that more and more organizations are using data in a predictive way—allowing the organization to make predictions on future events (such as the likelihood of turnover noted above) or to change or refocus efforts in ways that are more productive.

That said, despite an abundance of data available, sometimes it’s difficult to know what to do with it all. How can organizations use the data they collect in ways that truly impact the bottom line? Let’s take a look at a few examples.

How Some Companies Use Talent Analytics to Their Advantage

Here are a few examples of how companies today are using data in HR-related decision making:

Companies are going beyond more quantitative metrics like cost-to-hire and time-to-hire. They’re progressing to more qualitative things like quality of hire. A deeper amount of data—and a more thorough analysis of that data—is required to make more qualitative assessments with it.

Employee surveys are being used more to get input and help with the decision-making process. Employee surveys may seem simplistic, but they provide dozens more data points to aid in assessments. Some organizations are even taking this to the level of assessing how the employee engagement level directly impacts the bottom line—and how incremental changes in engagement can impact profit levels.

Employers are analyzing data related to how effective benefit programs are. For example, if an employer has opted to implement an employee wellness program, this program likely has several positive impacts for both the employer and the employees. The employer may be looking forward to decreased absences and a reduction in overall healthcare benefit spending over time. With a greater focus on data and analytics, more companies are able to measure such goals; they’re checking to see how effective their programs are at returning their intended result.

Some employers are assessing how their hiring criteria actually impact future job performance. For example, an organization might have a job experience requirement of 5 years in a specific field. It might also have a desired qualification that the individual be a team player with a willingness to share responsibilities. If the company wants to see which metric is more likely to result in a successful tenure with the employer, it can look at variances in the employee skill sets at hire and compare each skill with the future successes at the organization. This could result in changes to which hiring criteria are given the most weight.

Obviously, some of these types of analysis will be better suited for organizations with a higher number of employees, as the data may not be statistically significant with too few people to assess. But that doesn’t mean that talent analytics cannot be utilized at smaller organizations; it just means that the organization will need to be aware of the limitations if the data set is smaller.

How is your organization utilizing talent analytics to make better decisions? Have you changed which hiring characteristics you weigh most heavily, for example? Or taken more proactive steps to reduce turnover based on specific data inputs you’ve found? Tell your stories in the comments below.

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